Geometric modeling and recognition of elongated regions in aerial images

The authors present a curve-fitting method for the recovery of elongated regions in aerial images by analyzing the local image features that constitute these regions. Elongation is defined in terms of an axial model that describes a broad class of objects found in aerial scenes. The authors fit the model using a perceptual strip clustering that maintains constraints on feature connectivity and curvilinear alignment. Connectivity is obtained from internal representations of closest-point properties, specifically, a thresholded version of the Gabriel graph. This is shown to offer the advantages of both the minimum spanning tree and the Delauney triangulation, while being easily computable from finite neighborhood operations. An iterative curve fitting is used for the clustering of maximally elongated partitions, incorporating the perceptual constraints. This has the ability to interpolate across missing elements with a smooth curve, while ignoring nearby complicating structures and systematic gross noise. The general parametric strip model is described, with specific algorithms and examples given for lines and circles. A complete analysis of linear shape in low-level region segmentations is presented, with results for a suburban road network.<
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